These examples illustrate the main features of the releases of scikit-learn.
Examples concerning the sklearn.cluster.bicluster module.
Examples illustrating the calibration of predicted probabilities of classifiers.
General examples about classification algorithms.
Examples concerning the sklearn.cluster module.
Examples concerning the sklearn.covariance module.
Examples concerning the sklearn.cross_decomposition module.
Examples concerning the sklearn.datasets module.
Examples concerning the sklearn.tree module.
Examples concerning the sklearn.decomposition module.
Examples concerning the sklearn.ensemble module.
Applications to real world problems with some medium sized datasets or interactive user interface.
Examples concerning the sklearn.feature_selection module.
Examples concerning the sklearn.mixture module.
Examples concerning the sklearn.gaussian_process module.
Examples concerning the sklearn.linear_model module.
Examples related to the sklearn.inspection module.
Examples concerning the sklearn.kernel_approximation module.
Examples concerning the sklearn.manifold module.
Miscellaneous and introductory examples for scikit-learn.
Examples concerning the sklearn.impute module.
Examples related to the sklearn.model_selection module.
Examples concerning the sklearn.multioutput module.
Examples concerning the sklearn.neighbors module.
Examples concerning the sklearn.neural_network module.
Examples of how to compose transformers and pipelines from other estimators. See the User Guide.
Examples concerning the sklearn.preprocessing module.
Examples concerning the sklearn.semi_supervised module.
Examples concerning the sklearn.svm module.
Exercises for the tutorials
Examples concerning the sklearn.feature_extraction.text module.
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Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/auto_examples/index.html